As the market for digital cellular telephones increases the importance of noise suppression in speech processing also increases. Users of digital telephones expect high performance in noisy conditions such as operation in a moving automobile.
One common noise suppression technique is the well known spectral subtraction method. With this method, the noise signal, N(t) is considered to be stationary and independent of the received signal, X(t), such that:X(t)=S(t)+N(t)Where S(t) is noise-free speech signal.
Given the above equation, it is possible to calculate the power spectrum of the signal and subtract the noise spectrum. This is typically accomplished by sampling the input signal, estimating the power spectrum by applying the fast Fourier transform algorithm to the data sample, removing the noise component and then applying the inverse fast Fourier transform to recover the time domain clean speech signal.
This technique significantly increases the quality of the sampled speech but has the drawback of adding a distortion to the signal, often heard as a musical tone or noise.
To solve this problem, smoothed noise suppression techniques have been developed. An example of this technique is disclosed in U.S. Pat. No. 5,206,395, issued to Asslan, et al. and entitled “Adaptive Weiner Filtering Using a Dynamic Suppression Factor.” This method improves spectral subtraction by clamping attenuation to limit suppression for input with small signal-to-noise ratios, by smoothing noisy speech and noisy spectral through use of a filter, by increasing noise estimates to avoid filter fluctuations, and by updating a noise spectrum estimate from the preceding frame using the noisy speech spectrum. This approach eliminates musical tones or noise but has the draw back of being computationally expensive.